As GenAI systems move into production, traditional support models often fall short. Without GenAI-specific channels, feedback loops, and triage processes, issues linger and teams struggle to learn from real usage.
To win, your GenAI solutions must be supported by scalable channels, actionable feedback, and teams trained to handle GenAI-specific issues.
When GenAI support is treated like standard software support, problems compound quickly:
- Limited access: Users lack clear channels to get help or report GenAI-specific issues.
- Weak feedback loops: Valuable user feedback is not captured or routed back into improvement cycles.
- Inefficient resolution: Support teams lack tooling and guidance to triage, troubleshoot, and escalate GenAI issues.
These challenges reduce user confidence, slow iteration, and increase operational burden.
In this hands-on workshop, your team designs and operationalizes effective support models tailored to GenAI solutions.
- Expand support channels to better serve users of GenAI-powered features.
- Embed feedback capture directly into GenAI user experiences.
- Create practical troubleshooting guides specific to GenAI behaviors and failures.
- Automate support triage and escalation for common GenAI issues.
- Train teams to confidently support and operate GenAI use cases.
- Expanding Support Channels for GenAI Solutions
- Embedding Feedback Capture in UX
- Creating GenAI Troubleshooting Guides
- Automating Support Triage and Escalation
- Training Teams on GenAI-Specific Support Needs
- Design support channels aligned to how users actually experience GenAI features.
- Capture actionable feedback that feeds continuous improvement.
- Enable faster issue resolution with GenAI-specific troubleshooting assets.
- Reduce support load through automated triage and escalation.
- Equip teams to confidently support GenAI solutions in production.
Who Should Attend:
Solution Essentials
Facilitated workshop (in-person or virtual)
4 hours
Intermediate
Support workflows, feedback capture patterns, triage automation examples, and training artifacts